



from scipy.stats import spearmanr

import datasets


a      :  Optional[Any]			     =			'\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

a      :  Tuple			     =			'\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

a      :  List[str]			     =			r'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,		_KWARGS_DESCRIPTION					)
class 				_a				(   datasets.Metric					):

			def      __snake_case       (self					)       ->   Union[str, Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
						        {
						            """predictions""": datasets.Value("""float"""					),
						            """references""": datasets.Value("""float"""					),
						        }					),   reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""],   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=False					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[int]							      =     spearmanr(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						if return_pvalue:
									return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
						else:
									return {"spearmanr": results[0]}











import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    AlbertForMaskedLM,
	    AlbertForMultipleChoice,
	    AlbertForPreTraining,
	    AlbertForQuestionAnswering,
	    AlbertForSequenceClassification,
	    AlbertForTokenClassification,
	    AlbertModel,
	)
	from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=13,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=36,   SCREAMING_SNAKE_CASE_=6,   SCREAMING_SNAKE_CASE_=6,   SCREAMING_SNAKE_CASE_=6,   SCREAMING_SNAKE_CASE_=37,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=None,   )       ->   Tuple:
						UpperCAmelCase_:		Any							      =     parent
						UpperCAmelCase_:		Union[str, Any]							      =     batch_size
						UpperCAmelCase_:		int							      =     seq_length
						UpperCAmelCase_:		Any							      =     is_training
						UpperCAmelCase_:		str							      =     use_input_mask
						UpperCAmelCase_:		Dict							      =     use_token_type_ids
						UpperCAmelCase_:		Any							      =     use_labels
						UpperCAmelCase_:		Any							      =     vocab_size
						UpperCAmelCase_:		Dict							      =     embedding_size
						UpperCAmelCase_:		Dict							      =     hidden_size
						UpperCAmelCase_:		Dict							      =     num_hidden_layers
						UpperCAmelCase_:		Dict							      =     num_hidden_groups
						UpperCAmelCase_:		int							      =     num_attention_heads
						UpperCAmelCase_:		List[str]							      =     intermediate_size
						UpperCAmelCase_:		Any							      =     hidden_act
						UpperCAmelCase_:		Dict							      =     hidden_dropout_prob
						UpperCAmelCase_:		Any							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		Tuple							      =     max_position_embeddings
						UpperCAmelCase_:		Tuple							      =     type_vocab_size
						UpperCAmelCase_:		List[Any]							      =     type_sequence_label_size
						UpperCAmelCase_:		Any							      =     initializer_range
						UpperCAmelCase_:		int							      =     num_labels
						UpperCAmelCase_:		Optional[int]							      =     num_choices
						UpperCAmelCase_:		Tuple							      =     scope

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)

						UpperCAmelCase_:		Tuple							      =     None
						if self.use_input_mask:
									UpperCAmelCase_:		Union[str, Any]							      =     random_attention_mask([self.batch_size, self.seq_length]					)

						UpperCAmelCase_:		Dict							      =     None
						if self.use_token_type_ids:
									UpperCAmelCase_:		int							      =     ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size					)

						UpperCAmelCase_:		Tuple							      =     None
						UpperCAmelCase_:		Dict							      =     None
						UpperCAmelCase_:		Tuple							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Optional[int]							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)
									UpperCAmelCase_:		Dict							      =     ids_tensor([self.batch_size, self.seq_length],   self.num_labels					)
									UpperCAmelCase_:		List[Any]							      =     ids_tensor([self.batch_size],   self.num_choices					)

						UpperCAmelCase_:		Optional[int]							      =     self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def      __snake_case       (self					)       ->   Optional[Any]:
						return AlbertConfig(
						    vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   type_vocab_size=self.type_vocab_size,   initializer_range=self.initializer_range,   num_hidden_groups=self.num_hidden_groups,   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		int							      =     AlbertModel(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[int]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)
						self.parent.assertEqual(result.pooler_output.shape,   (self.batch_size, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   str:
						UpperCAmelCase_:		Any							      =     AlbertForPreTraining(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   sentence_order_label=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.prediction_logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)
						self.parent.assertEqual(result.sop_logits.shape,   (self.batch_size, config.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						UpperCAmelCase_:		Optional[int]							      =     AlbertForMaskedLM(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     AlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Any							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   start_positions=SCREAMING_SNAKE_CASE_,   end_positions=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length)					)
						self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Any:
						UpperCAmelCase_:		Optional[Any]							      =     self.num_labels
						UpperCAmelCase_:		List[Any]							      =     AlbertForSequenceClassification(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		str							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		List[str]							      =     self.num_labels
						UpperCAmelCase_:		Union[str, Any]							      =     AlbertForTokenClassification(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		Dict							      =     self.num_choices
						UpperCAmelCase_:		Dict							      =     AlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		str							      =     input_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		str							      =     token_type_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[Any]							      =     input_mask.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		Union[str, Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices)					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		List[str]							      =     config_and_inputs
						UpperCAmelCase_:		Tuple							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_torch
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (
			        AlbertModel,
			        AlbertForPreTraining,
			        AlbertForMaskedLM,
			        AlbertForMultipleChoice,
			        AlbertForSequenceClassification,
			        AlbertForTokenClassification,
			        AlbertForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			A      		=	(
			    {
			        '''feature-extraction''': AlbertModel,
			        '''fill-mask''': AlbertForMaskedLM,
			        '''question-answering''': AlbertForQuestionAnswering,
			        '''text-classification''': AlbertForSequenceClassification,
			        '''token-classification''': AlbertForTokenClassification,
			        '''zero-shot''': AlbertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			A      		=	True

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=False					)       ->   Any:
						UpperCAmelCase_:		Tuple							      =     super()._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   return_labels=SCREAMING_SNAKE_CASE_					)

						if return_labels:
									if model_class in get_values(SCREAMING_SNAKE_CASE_					):
												UpperCAmelCase_:		Dict							      =     torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.seq_length),   dtype=torch.long,   device=SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     torch.zeros(
												    self.model_tester.batch_size,   dtype=torch.long,   device=SCREAMING_SNAKE_CASE_					)
						return inputs_dict

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Any							      =     AlbertModelTester(self					)
						UpperCAmelCase_:		Union[str, Any]							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   hidden_size=37					)

			def      __snake_case       (self					)       ->   List[Any]:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		List[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		int							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Tuple							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Optional[int]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_:		Union[str, Any]							      =     type
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   List[Any]:
						for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:		Dict							      =     AlbertModel.from_pretrained(SCREAMING_SNAKE_CASE_					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)







@require_torch
class 				_a				(   unittest.TestCase					):

			@slow
			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		str							      =     AlbertModel.from_pretrained("""albert-base-v2"""					)
						UpperCAmelCase_:		List[str]							      =     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
						UpperCAmelCase_:		List[str]							      =     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
						with torch.no_grad():
									UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_					)[0]
						UpperCAmelCase_:		List[str]							      =     torch.Size((1, 11, 768)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     torch.tensor(
						    [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]					)

						self.assertTrue(torch.allclose(output[:, 1:4, 1:4],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)






